This projects integrates IPython Notebook, a interactive computational environment, with Galaxy.
We hope to make Galaxy more attractive for bioinformaticians and to combine the power of both projects to unlock creativity in data analysis, but also in Next-Generation-Training courses.
Check this
Galaxy IPython Video
to get an idea of its potential.

Software and Libraries for Download

metaMIR - a framework to predict in human interactions between microRNAs (miRNA) and clusters of genes

The Explicit Decomposition with Neighborhoods (EDeN) is a
decompositional kernel based on the Neighborhood Subgraph Pairwise
Distance Kernel (NSPDK) that can be used to induce an explicit
feature representation for graphs. This in turn allows the adoption of
machine learning algorithm to perform supervised and unsupervised
learning task in a scalable way (e.g. fast stochastic gradient
descent methods in classification).
Among the novelties introduced in EDeN is the ability to take in input
real vector labels and to process weighted graphs.

BlockClust is an efficient approach to detect transcripts with similar
processing patterns. We propose a novel way to encode expression profiles
in compact discrete structures, which can then be processed using fast
graph-kernel techniques. BlockClust allows both clustering and
classification of small non-coding RNAs.

During the last few years, several new small regulatory RNAs (sRNAs)
have been discovered in bacteria. Most of them act as post-transcriptional
regulators by base pairing to a target mRNA, causing translational
repression or activation, or mRNA degradation. Numerous sRNAs have
already been identified, but the number of experimentally verified
targets is considerably lower. Consequently, computational target
prediction is in great demand. Many existing target prediction
programs neglect the accessibility of target sites and the existence
of a seed, while other approaches are either specialized to certain
types of RNAs or too slow for genome-wide searches.

IntaRNA,
developed by Prof. Backofen's bioinformatics group at
Freiburg University, is a general and fast approach to the
prediction of RNA-RNA interactions incorporating both the
accessibility of interacting sites as well as the existence of a
user-definable seed interaction. We successfully applied IntaRNA to
the prediction of bacterial sRNA targets and determined the exact
locations of the interactions with a higher accuracy than competing
programs.

A tool for pairwise and multiple, global and local alignment of RNA
with simultaneous folding. LocARNA requires only RNA sequences as
input and will simultaneously fold and align the input sequences.
Specifications of additional constraints or fixed input structures
are possible. For the folding it makes use of a very realistic
energy model for RNAs as it is by RNAfold of the Vienna RNA package
(or Zuker's mfold). For the alignment it features RIBOSUM-like
similarity scoring and realistic gap cost.

CARNA is a tool for multiple alignment of RNA molecules. CARNA
requires only the RNA sequences as input and will compute base pair
probability matrices and align the sequences based on their full
ensembles of structures. Alternatively, you can also provide base
pair probability matrices (dot plots in .ps format) or fixed
structures (as annotation in the FASTA alignment) for your sequences.
If you provide fixed structures, only those structures and not the
entire ensemble of possible structures is aligned. In contrast to
LocARNA, CARNA does not pick the most likely consensus structure,
but computes the alignment that fits best to all likely structures
simultaneously. Hence, CARNA is particularly useful when aligning
RNAs like riboswitches, which have more than one stable structure.
Also, CARNA is not limited to nested structures, but is able to
align arbitrary pseudoknots.

C++ implementation to find the longest common subsequence of exact pattern matchings (LCS-EPM problem)
of two RNAs given with their primary and secondary structure (mfe-structure is used if no structure is available).
Source is available as [tar.gz] as linked above;
compiles with Gnu C++ Compiler 4.x.
Copyright by Steffen Heyne, 2008-2013.
If you use ExpaRNA, please cite our article.

To use ExpaRNA, you need the library of the Vienna RNA Package
that can be downloaded here.
ExpaRNA compiles also under Cygwin for Windows!
usage example: 'ExpaRNA Examples/HCVirus_IRES_RNAs.fa'
New in 1.0: bugfix, >1000 constraints in output file possible!